Run structural analysis

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description must carry the burden. It details return values (displacements, reactions, etc.) but does not mention side effects like model mutation or data persistence. The description is informative but incomplete regarding behavioral impact.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences efficiently cover purpose, engine options, and outputs. Every clause adds value, and the structure is front-loaded with the core action. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite good detail on engines and outputs, the description does not clarify whether the results are directly returned or stored for later retrieval (e.g., via get_results). This gap could confuse an agent unfamiliar with the workflow. A brief note about result storage would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema describes 2 of 3 parameters (engine and subdivisions) but the description adds significant meaning to the 'engine' parameter by explaining each option's solver and accuracy. The 'model' parameter lacks explanation, but the overall added value is strong.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Run linear-static analysis' and specifies the three engine options (beam, calculix, both) with their characteristics. This distinguishes it from sibling tools that focus on model construction (add_loads, add_members) or data export.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage after model setup but does not explicitly state prerequisites or when not to use. No comparison with alternatives is given, leaving the agent to infer based on sibling tool names.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.